artifiticial intelligence
artifiticial intelligence

AI is intelligence of machines and branch of computer science that aims to create it. AI consists of design of intelligent agents, which is a program that perceives its environment and takes action that maximizes its chance of success.

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Scientists have proposed two major “consensus” definitions of intelligence:

(i) from Mainstream Science on Intelligence (1994).
A very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings- making sense” of things, or “figuring out” what to do.
(ii) from Intelligence: Known and Unknowns (1995)
Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, [and] to overcome obstacles by taking thought. Although these individual differences can be substantial, they are never entirely consistent: a given person’s intellectual performance will vary on different occasions, in different domains, as judged by different criteria. Concepts of “intelligence” are attempts to clarify and organize this complex set of phenomena.
Thus, intelligence is:
– the ability to reason
– the ability to understand
– the ability to create
– the ability to Learn from experience
– the ability to plan and execute complex tasks.

What is Artificial Intelligence?

“Giving machines ability to perform tasks normally associated with human

AI is intelligence of machines and branch of computer science that aims to create it. AI consists of design of intelligent agents, which is a program that perceives its environment and takes action that maximizes its chance of success. With Ai it comes issues like deduction, reasoning, problem solving, knowledge representation, planning, learning, natural language processing, perceptron, etc.

“Artificial Intelligence is the part of computer science concerned with designing intelligence computer systems, that is, systems that exhibit the characteristics we associate with intelligence in human behavior.”

Brief history of AI

–1943: Warren Mc Culloch and Walter Pitts: a model of artificial boolean neurons to
perform computations.
– First steps toward connectionist computation and learning (Hebbian learning).
– Marvin Minsky and Dann Edmonds (1951) constructed the first neural network
– 1950: Alan Turing’s “Computing Machinery and Intelligence”
– First complete vision of AI.

The birth of AI (1956):
-Dartmouth Workshop bringing together top minds on automata theory, neural nets and
the study of intelligence.
– Allen Newell and Herbert Simon: The logic theorist (first nonnumeric thinking
program used for theorem proving)
– For the next 20 years the field was dominated by these participants.
Great expectations (1952-1969):
–Newell and Simon introduced the General Problem Solver.
– Imitation of human problem-solving
–Arthur Samuel (1952-) investigated game playing (checkers ) with great success.
–John McCarthy(1958-) :
– Inventor of Lisp (second-oldest high-level language)
– Logic oriented, Advice Taker (separation between knowledge and reasoning)
–Marvin Minsky (1958 -):
– Introduction of micro worlds that appear to require intelligence to solve: e.g. blocks world.
– Anti-logic orientation, society of the mind.

Collapse in AI research (1966 – 1973):
– Progress was slower than expected.
– Unrealistic predictions.
– Some systems lacked scalability.
– Combinatorial explosion in search.
– Fundamental limitations on techniques and representations.
– Minsky and Papert (1969) Perceptrons.
AI revival through knowledge-based systems (1969-1970):
– General-purpose vs. domain specific
E.g. the DENDRAL project (Buchanan et al. 1969)
First successful knowledge intensive system.
– Expert systems
MYCIN to diagnose blood infections (Feigenbaum et al.)
Introduction of uncertainty in reasoning.
– Increase in knowledge representation research.
Logic, frames, semantic nets, …
AI becomes an industry (1980 – present):
– R1 at DEC (McDermott, 1982)
– Fifth generation project in Japan (1981)
– American response …
Puts an end to the AI winter.
Connectionist revival (1986 – present): (Return of Neural Network):
– Parallel distributed processing (RumelHart and McClelland, 1986); backprop.
AI becomes a science (1987 – present):
– In speech recognition: hidden markov models
– In neural networks
– In uncertain reasoning and expert systems: Bayesian network formalism
The emergence of intelligent agents (1995 – present):
– The whole agent problem:
“How does an agent act/behave embedded in real environments with continuous
sensory inputs”

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